1. Field of the Invention
This invention relates generally to a method and system for comparing and merging fault models and, more particularly, to a method and system for comparing and merging fault models derived from different data sources which represents each fault model as a bipartite weighted graph, identifies common failure modes and symptoms between the graphs, compares fault models using a graph matching method, and produces a merged and updated fault model as output.
2. Discussion of the Related Art
Modern vehicles are complex electro-mechanical systems that employ many sub-systems, components, devices, and modules, which pass operating information between and among each other using sophisticated algorithms and data buses. As with anything, these types of devices and algorithms are susceptible to errors, failures and faults that can affect the operation of the vehicle. To help manage this complexity, vehicle manufacturers develop fault models, which match the various failure modes with the symptoms exhibited by the vehicle.
Vehicle manufacturers commonly develop fault models from a variety of different data sources. These data sources include engineering data, service procedure documents, text verbatim from customers and repair technicians, warranty data, and others. While all of these fault models show the correlations between failure modes and symptoms, there are enough differences between the fault models that it is difficult to compare and combine them directly. The differences include using different terminology to mean the same thing, extra items or missing items in one fault model or another, and even different correlations between a common failure mode and symptom pair. These differences have traditionally meant that the various fault models are used independently of one another, and are never compared in sufficient detail to determine where there may be synergies or inconsistencies between them. As a result, service procedure documents and onboard and off-board diagnostic tools may not take advantage of all known correlations between failure modes and symptoms.
There is a need for a method for comparing and merging fault models which are developed from different data sources. Such a method could not only create an integrated fault model for improved fault diagnosis by various downstream users of the model, but could also be used to enhance service procedures, detect inappropriate repairs at service shops, and improve diagnostic comparisons across vehicle platforms.